Why Your RAG System Doesn't Need Embeddings
📰 Hackernoon
A well-designed RAG system with BM25 can outperform vector-based search, making embeddings less necessary
Action Steps
- Benchmark different search algorithms (BM25, vector, hybrid) on your corpus
- Evaluate the performance of your LLM with BM25
- Consider the impact of ingestion quality and model choice on search results
Who Needs to Know This
Developers and AI engineers working on RAG systems can benefit from understanding the trade-offs between different search algorithms and focus on improving ingestion quality and model choice
Key Insight
💡 A good agent with BM25 can achieve better results than a single-pass vector query
Share This
🚀 Ditch the embeddings? BM25 can outperform vector search in RAG systems!
DeepCamp AI